Action-Conditioned Frame Prediction Without Discriminator

Show simple item record Valencia, David Williams, Henry MacDonald, Bruce Qiao, Ting 2022-04-12T04:23:51Z 2022-04-12T04:23:51Z 2022-2-2
dc.identifier.citation Lecture Notes in Computer Science 13163: 324-337. 02 Feb 2022
dc.identifier.isbn 9783030954666
dc.identifier.issn 0302-9743
dc.description.abstract Predicting high-quality images that depend on past images and external events is a challenge in computer vision. Prior proposals have tried to solve this problem; however, their architectures are complex, unstable, or difficult to train. This paper presents an action-conditioned network based upon Introspective Variational Autoencoder (IntroVAE) with a simplistic design to predict high-quality samples. The proposed architecture combines features of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) with encoding and decoding layers that can self-evaluate the quality of predicted frames; no extra discriminator network is needed in our framework. Experimental results with two data sets show that the proposed architecture could be applied to small and large images. Our predicted samples are comparable to the state-of-the-art GAN-based networks.
dc.publisher Springer International Publishing
dc.relation.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use
dc.title Action-Conditioned Frame Prediction Without Discriminator
dc.type Conference Item
dc.identifier.doi 10.1007/978-3-030-95467-3_24
pubs.begin-page 324
pubs.volume 13163 2022-03-15T19:06:16Z
dc.rights.holder Copyright: The author en
pubs.end-page 337
pubs.publication-status Published
dc.rights.accessrights en
pubs.elements-id 889101
dc.identifier.eissn 1611-3349 2022-2-2

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